creators_name: Saha, Sudipto
creators_name: Raghava, G.P.S.
type: article
datestamp: 2011-12-08 19:41:02
lastmod: 2011-12-08 19:41:02
metadata_visibility: show
title: BTXpred: prediction of bacterial toxins.
ispublished: pub
subjects: QH301
full_text_status: public
keywords:  bacterial toxins, exotoxins, endotoxins, BTXpred, prediction server 
note: Supplementary information is available at http://www.imtech.res.in/raghava/btxpred/supplementary.html.
 


abstract: This paper describes a method developed for predicting bacterial toxins from their amino acid sequences. All the modules, developed in this study, were trained and tested on a non-redundant dataset of 150 bacterial toxins that included 77 exotoxins and 73 endotoxins. Firstly, support vector machines (SVM) based modules were developed for predicting the bacterial toxins using amino acids and dipeptides composition and achieved an accuracy of 96.07% and 92.50%, respectively. Secondly, SVM based modules were developed for discriminating entotoxins and exotoxins, using amino acids and dipeptides composition and achieved an accuracy of 95.71% and 92.86%, respectively. In addition, modules have been developed for classifying the exotoxins (e.g. activate adenylate cyclase, activate guanylate cyclase, neurotoxins) using hidden Markov models (HMM), PSI-BLAST and a combination of the two and achieved overall accuracy of 95.75%, 97.87% and 100%, respectively. Based on the above study, a web server called 'BTXpred' has been developed, which is available at http://www.imtech.res.in/raghava/btxpred/. Supplementary information is available at http://www.imtech.res.in/raghava/btxpred/supplementary.html.
date: 2007
date_type: published
publication: In silico biology
volume: 7
number: 4-5
publisher: Bioinformatiob System eV.
pagerange: 405-412
refereed: TRUE
issn: 1386-6338
official_url: http://www.bioinfo.de/isb/2007070028/
related_url_url: http://www.bioinfo.de/isb/2007070028/
related_url_type: pub
citation:   Saha, Sudipto and Raghava, G.P.S.  (2007) BTXpred: prediction of bacterial toxins.  In silico biology, 7 (4-5).  pp. 405-412.  ISSN 1386-6338     
document_url: http://crdd.osdd.net/open/606/1/raghavasilico.mht